code: def prepare_data(
      data_file,
      output_file,
      word2vec_file,
      http_proxy=None,
      https_proxy=None,
  ):
      """
      Extract for each noun-phrase a feature vector (W2V, WordNet, Wikidata, NPMI, UCI).
      Write the feature vectors to --output specifies local path
  
      Args:
          data_file(str): file_path to input data
          output_file(str): file_path to output processed data
          word2vec_file(str): file_path to word2vec model
          http_proxy(str): http_proxy
          https_proxy(str): https_proxy
      """
      # init_resources:
      global wordnet, wikidata, word2vec
      # pylint: disable=no-member
      wordnet = fe.Wordnet()
      # pylint: disable=no-member
      wikidata = fe.Wikidata(http_proxy, https_proxy)
      print("Start loading Word2Vec model (this might take a while...)")
      # pylint: disable=no-member
      word2vec = fe.Word2Vec(word2vec_file)
      print("Finish loading feature extraction services")
      reader_list = read_csv_file_data(data_file)
      np_dic = {}
      np_list = []
      for row in reader_list:
          np_dic[row[0]] = row[1]
          np_list.append(row[0])
      p = Pool(10)
      np_feature_vectors = list(
          tqdm(
              p.imap(build_feature_vector, np_list), total=len(np_list)
          )
      )  # , desc="np feature extraction status"))
      write_to_csv(output_file, np_feature_vectors, np_dic, np_list)
  
imports: import argparse
  import csv
  import math
  import os
  from multiprocessing import Pool
  import feature_extraction
  import numpy
  from tqdm import tqdm
  from nlp_architect.utils.io import validate_existing_filepath, validate_parent_exists, validate_proxy_path
label_desc: Use image node_id 7 for calling a global function with example usage: prepare_data(data_file, output_file, word2vec_file, http_proxy, https_proxy) without return types